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Conference Paper: An adaptive RBF neural network model for evoked potential estimation

TitleAn adaptive RBF neural network model for evoked potential estimation
Authors
Fung, SMChan, FHYLam, FKPoon, PWF
KeywordsMedical sciences
Computer applications
Issue Date1997
PublisherIEEE.
Citation
The 19th IEEE Engineering in Medicine and Biology Society Conference Proceedings, Chicago, Illinois, USA, 30 October - 2 November 1997, v. 3, p. 1097-1099 How to Cite?
DOI: http://dx.doi.org/10.1109/IEMBS.1997.756542
AbstractA method for evoked potential estimation based on an adaptive radial basis function neural network (RBFNN) model is presented in this paper. During training, the number of hidden nodes (number of RBFs) and model parameters are adjusted to fit the target signal which is obtained by averaging. In order to reduce computational complexity and the influence of noise in estimating single-trial evoked potential (EP), the number of hidden nodes is also minimized in training. After training, both peak latency and amplitude, being distinctive features of an EP, are characterized by center and height of the corresponding RBF respectively. In EP estimation, an adaptive algorithm is employed to track the peaks from trial to trial by adapting the center and height of RBFs directly. The adaptive RBFNN is tested on a computer simulated data set and clinical EP recording. Our proposed algorithm is suitable for tracking EP waveform variations.
Persistent Identifierhttp://hdl.handle.net/10722/46054
ISSN
1557-170X
2020 SCImago Journal Rankings: 0.282

 

DC FieldValueLanguage
dc.contributor.authorFung, SMen_HK
dc.contributor.authorChan, FHYen_HK
dc.contributor.authorLam, FKen_HK
dc.contributor.authorPoon, PWFen_HK
dc.date.accessioned2007-10-30T06:41:33Z-
dc.date.available2007-10-30T06:41:33Z-
dc.date.issued1997en_HK
dc.identifier.citationThe 19th IEEE Engineering in Medicine and Biology Society Conference Proceedings, Chicago, Illinois, USA, 30 October - 2 November 1997, v. 3, p. 1097-1099en_HK
dc.identifier.issn1557-170Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/46054-
dc.description.abstractA method for evoked potential estimation based on an adaptive radial basis function neural network (RBFNN) model is presented in this paper. During training, the number of hidden nodes (number of RBFs) and model parameters are adjusted to fit the target signal which is obtained by averaging. In order to reduce computational complexity and the influence of noise in estimating single-trial evoked potential (EP), the number of hidden nodes is also minimized in training. After training, both peak latency and amplitude, being distinctive features of an EP, are characterized by center and height of the corresponding RBF respectively. In EP estimation, an adaptive algorithm is employed to track the peaks from trial to trial by adapting the center and height of RBFs directly. The adaptive RBFNN is tested on a computer simulated data set and clinical EP recording. Our proposed algorithm is suitable for tracking EP waveform variations.en_HK
dc.format.extent279814 bytes-
dc.format.extent13817 bytes-
dc.format.extent8841 bytes-
dc.format.mimetypeapplication/pdf-
dc.format.mimetypetext/plain-
dc.format.mimetypetext/plain-
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.rights©1997 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectMedical sciencesen_HK
dc.subjectComputer applicationsen_HK
dc.titleAn adaptive RBF neural network model for evoked potential estimationen_HK
dc.typeConference_Paperen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1557-170X&volume=3&spage=1097&epage=1099&date=1997&atitle=An+adaptive+RBF+neural+network+model+for+evoked+potential+estimationen_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/IEMBS.1997.756542en_HK
dc.identifier.hkuros34854-
dc.identifier.issnl1557-170X-

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